Huagong Xuebao/CIESC Journal最新文献

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Data-Driven Modeling and Bayesian Optimization for the Formaldehyde-Acetylene Reaction in a Slurry Bed Reactor 浆床反应器中甲醛-乙炔反应的数据驱动建模与贝叶斯优化
Huagong Xuebao/CIESC Journal Pub Date : 2025-12-05 DOI: 10.53941/sce.2025.100007
Zuo-Qian Jihou, Xiaoqi Liu, Hui‐Long Wei, Zheng-Hong Luo
{"title":"Data-Driven Modeling and Bayesian Optimization for the Formaldehyde-Acetylene Reaction in a Slurry Bed Reactor","authors":"Zuo-Qian Jihou, Xiaoqi Liu, Hui‐Long Wei, Zheng-Hong Luo","doi":"10.53941/sce.2025.100007","DOIUrl":"https://doi.org/10.53941/sce.2025.100007","url":null,"abstract":"1,4-Butynediol (BYD), an essential intermediate for fine chemicals and polymer production, is primarily synthesized via formaldehyde-acetylene reaction. Kinetic experiments were conducted in a quasi-industrial slurry bed reactor under the conditions of 55–85 °C, 0–10.5 h, and pH 5–9 to obtain the time-resolved yield of 1,4-butynediol and the conversion of formaldehyde. The experimental results revealed that pH was a critical influencing factor on reaction performance, while it can not be directly coupled into mechanistic kinetic models. Therefore, four machine learning models, i.e., random forest (RF), extremely randomized trees (Extra Trees, ET), light gradient boosting machine (LightGBM) and extreme gradient boosting (XGBoost) were employed to establish data-driven models that can directly capture the pH influence. The 84 experimental data points were augmented to 1023 samples by interpolation and extrapolation method, then the dataset was split into training, validation, and testing subsets in a 6:2:2 ratio. The training results demonstrated that the XGBoost model exhibited the best generalization ability and stability, achieving the highest average coefficient of determination (R2) for formaldehyde conversion (0.9847 ± 0.0022) and 1,4-butynediol yield (0.9773 ± 0.0035), and the mean absolute error for both targets was less than 0.027. Finally, the XGBoost model was coupled with Bayesian optimization to search the optimal process parameters.","PeriodicalId":34892,"journal":{"name":"Huagong Xuebao/CIESC Journal","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://media.sciltp.com/articles/2512002419/2512002419.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147333430","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Extracting Technology Upcycling Toward Useful Metallic Materials From Mineral Wastes and Pollutant Soil by Ultrasound Washing 超声波洗涤从矿物废弃物和污染土壤中提取有用金属材料的技术升级利用
Huagong Xuebao/CIESC Journal Pub Date : 2024-11-08 DOI: 10.4018/979-8-3693-0003-9.ch016
Phan Minh-Thu
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引用次数: 0
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